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Optimizing Stabilizer Parities for Improved Logical Qubit Memories

Physical Review Letters 2021年第24期127卷 240501-240501页

作者： Dripto M. Debroy Laird Egan Crystal Noel Andrew Risinger Daiwei Zhu Debopriyo Biswas Marko Cetina Chris Monroe Kenneth R. Brown Department of Physics Duke University Durham North Carolina 27708 USA Joint Quantum Institute Center for Quantum Information and Computer Science and Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA IonQ College Park Maryland 20740 USA

We study variants of Shor’s code that are adept at handling single-axis correlated idling errors, which are commonly observed in many quantum systems. By using the repetition code structure of the Shor’s code basis states, we calculate the logical channel applied to the encoded information when subjected to coherent and correlated single qubit idling errors, followed by stabilizer measurement. Changing the signs of the stabilizer generators allows us to change how the coherent errors interfere, leading to a quantum error-correcting code which performs as well as a classical repetition code of equivalent distance against these errors. We demonstrate a factor of 3.78±1.20 improvement of the logical T2* in a distance-3 logical qubit implemented on a trapped-ion quantum computer. Even-distance versions of our Shor-code variants are decoherence-free subspaces and fully robust to identical and independent coherent idling noise.

关键词： Quantum error correction Quantum information with trapped ions Quantum memories Trapped ions

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Single-shot Quantitative Gradient Phase Microscopy (QGPM) to Monitor Cellular Kinetics

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Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Mohammadi, Melika Haji Hur, Sun Woong Kwon, Minsung Samuel, Ashok Zachariah Bhargava, Rohit Department of Bioengineering University of Illinois Urbana-Champaign Urbana IL United States Beckman Institute for Advanced Science and Technology Urbana IL United States Departments of Chemical and Biomolecular Engineering Electrical and Computer Engineering Mechanical Science and Engineering and Chemistry Cancer Center at Illinois University of Illinois Urbana-Champaign Urbana IL United States

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Virtual histological staining of label-free autopsy tissue sections via deep learning

Virtual histological staining of label-free autopsy tissue s...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Li, Yuzhu Pillar, Nir Li, Jingxi Liu, Tairan Wu, Di Sun, Songyu Ma, Guangdong de Haan, Kevin Huang, Luzhe Zhang, Yijie Hamidi, Sepehr Urisman, Anatoly Haran, Tal Keidar Wallace, William Dean Zuckerman, Jonathan E. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States School of Physics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States Department of Pathology University of California San FranciscoCA94143 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

We present deep learning-based virtual staining of label-free autopsy tissue sections, eliminating severe autolysis-induced artifacts caused by delayed fixation inherent in traditional histochemical H&E staining. ... 详细信息

ISBN: (纸本)9781957171395

关键词： Electrooptical materials

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Label-free Imaging with Photonic Crystal Surface for Hematopoietic Stem Cell Differentiation

Label-free Imaging with Photonic Crystal Surface for Hematop...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Zhuo, Yue Choi, Ji Sun Marin, Thibault Yu, Hojeong Harley, Brendan A. Cunningham, Brian T. Gordon Center for Medical Imaging Massachusetts General Hospital BostonMA02114 United States Center for Systems Biology Massachusetts General Hospital BostonMA02114 United States Department of Radiology Harvard Medical School BostonMA02114 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

With the Photonic Resonator Outcoupler Microscopy (PROM), it is possible to detect and monitor weak-adhesive HSC adhesion without labeling. These findings indicate that PROM can be used to quantitatively and dynamical... 详细信息

ISBN: (纸本)9781957171340

关键词： Stem cells

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Virtual histological staining of label-free autopsy tissue sections via deep learning

Virtual histological staining of label-free autopsy tissue s...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Li, Yuzhu Pillar, Nir Li, Jingxi Liu, Tairan Wu, Di Sun, Songyu Ma, Guangdong de Haan, Kevin Huang, Luzhe Zhang, Yijie Hamidi, Sepehr Urisman, Anatoly Haran, Tal Keidar Wallace, William Dean Zuckerman, Jonathan E. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States School of Physics Xi an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States Department of Pathology University of California San FranciscoCA94143 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

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Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites

arXiv

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arXiv 2024年

作者： Xu, Hanliang Newlin, Nancy R. Kim, Michael E. Gao, Chenyu Kanakaraj, Praitayini Krishnan, Aravind R. Remedios, Lucas W. Khairi, Nazirah Mohd Pechman, Kimberly Archer, Derek Hohman, Timothy J. Jefferson, Angela L. Isgum, Ivana Huo, Yuankai Moyer, Daniel Schilling, Kurt G. Landman, Bennett A. Department of Computer Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Vanderbilt Genetics Institute Vanderbilt University School of Medicine NashvilleTN United States Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Departments of Neurology Johns Hopkins School of Medicine BaltimoreMD United States Department of Biomedical Engineering and Physics & Radiology and Nuclear Medicine University Medical Center Amsterdam University of Amsterdam Amsterdam Netherlands Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Biomedical Engineering Vanderbilt University NashvilleTN United States

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods’ effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05. © 2024, CC BY.

关键词： Magnetic resonance imaging

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Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

arXiv

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arXiv 2023年

作者： Yarach, Uten Chatnuntawech, Itthi Liao, Congyu Teerapittayanon, Surat Iyer, Siddharth Srinivasan Kim, Tae Hyung Haldar, Justin Cho, Jaejin Bilgic, Berkin Hu, Yuxin Hargreaves, Brian Setsompop, Kawin Radiologic Technology Department Associated Medical Sciences Chiang Mai University Chiang Mai Thailand National Nanotechnology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Computer Engineering Hongik University Seoul Korea Republic of Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Department of Bioengineering Stanford University StanfordCA United States Packard Electrical Engineering Building 350 Jane Stanford Way StanfordCA94305 United States

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Analog, In-memory Compute Architectures for Artificial Intelligence

arXiv

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arXiv 2023年

作者： Bowen, Patrick Regev, Guy Regev, Nir Pedroni, Bruno Umbria Hanson, Edward Chen, Yiran Neurophos 212 W Main St. #301 DurhamNC27701 United States Center for Metamaterials and Integrated Plasmonics Department of Electrical and Computer Engineering Duke University P.O. Box 90291 DurhamNC27708 United States AlephZero 5141 Beeman Ave. Valley VillageCA91607 United States Department of Electrical and Computer Engineering Ben-Gurion University of the Negev David Ben-Gurion Blvd. 1 Be'er Sheva Israel Department of Bioengineering UC San Diego 9500 Gilman Dr. La Jolla CA92093 United States Duke University Electrical and Computer Engineering Department Science Dr DurhamNC27710 United States

This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to single instruction, single data (scalar) machines specifically in the context of machine inference. The focus of the analysis is on how efficiency scales with the size, arithmetic intensity, and bit precision of the computation to be performed. It is shown that analog, in-memory computing architectures can approach arbitrarily high energy efficiency as both the problem size and processor size scales. © 2023, CC BY-NC-SA.

关键词： Energy efficiency

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Alteration of Cortical Somatosensory Feedback in Post-Stroke Movement Control

Alteration of Cortical Somatosensory Feedback in Post-Stroke...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rita Huan-Ting Peng Nishaal Parmar Jordan N. Williamson Parikshat Sirpal Beni E. Mulyana Mahmood Rajabtabar Darvish Joohwan Sung Evgeny V. Sidorov Yuan Yang Department of Bioengineering Grainger College of Engineering University of Illinois Urbana-Champaign Urbana IL USA Clinical Imaging Research Center Stephenson Family Clinical Research Institute Carle Foundation Hospital Urbana IL USA Department of Electrical and Computer Engineering University of Oklahoma Tulsa OK USA Department of Neurology University of Oklahoma Health Sciences Center Oklahoma City OK USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign Urbana IL USA Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Sensory feedback is of vital importance in motor control, yet is rarely studied in diseases which frequently result in motor deficiency, such as hemiparetic stroke. This study employs the laterality index (LI) of somatosensory evoked potentials (SEPs) to investigate whether sensory feedback is altered in hemiparetic stroke during movements of the paretic arm, with a hemispheric shift from the lesioned hemisphere toward contralesional hemisphere. Through experimental design involving the isometric lifting of the paretic arms during tactile finger stimulation and the analysis of LI in SEPs P50 and N100, we found: 1) increased contralesional sensory activity in stroke participants when they are receiving sensory input in their paretic hand for both P50 and N100 and 2) the contralesional N100 activity is enhanced when stroke participants are performing an isometric arm lifting task. These results indicate a timedependent shifting of sensory feedback from the sensorimotor areas in the lesioned side to the contralesional side of the stroke brain.

关键词： Somatosensory Neuroimaging Motor drives Magnetic resonance imaging Process control Motors Particle measurements Time measurement Indexes engineering in medicine and biology

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